Deep Kernel Learning For Reaction Outcome Prediction And Optimization

This article discusses the use of deep kernel learning (DKL) in predicting reaction outcomes in chemistry. DKL integrates neural networks and Gaussian processes to accurately predict outcomes and provide uncertainty estimates. The method is demonstrated on a dataset of Buchwald-Hartwig cross-coupling reactions, showing its potential for broadening the application of Gaussian processes in reaction development and reactivity prediction.